Anomaly detection is a process of monitoring of objects such as humans, non-humans, other objects, etc., for the purpose of identifying unusual patterns in behavior, activities, or other changing information. An anomaly is usually detected from time-series data using several existing techniques. Generally, the time-series data extracted using a sensor comprise unique signal patterns corresponding to the anomaly. Traditionally, anomaly detection in time-series data involves using prior knowledge of time window over which temporal analysis is done. Most anomaly detection techniques show poor performance when applied to univariate or multivariate time-series data, since these techniques require a pre-specified time window or data that needs to be pre-processed for these types of time-series data. Further, traditional process monitoring techniques use statistical measures such as cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) over a time window to detect changes in the underlying distribution. The length of this time window generally needs to be pre-determined, require extensive data pre-processing and the results greatly depend on this time window which in turn leads to degrading of the system performance. Current techniques implement prediction models to detect anomaly. However, these techniques do not incorporate inherent unpredictable patterns such as abrupt braking of the vehicle, rapid rise/fall in acceleration/deceleration of the vehicle, etc.